Lights, camera, action!

For our final project, we’re going to take a deep dive into the world of HBO movies and TV shows. HBO has been providing quality content to its viewers for decades, but have you ever been stuck on what to watch? By analyzing ratings, popularity, and your favorite actors/actresses, lets take a look at what HBO can offer to its loyal fans.

What we will be looking into?

• Movies and shows on HBO

• What does the distribution of Genres look like for both movies and shows?

• Correlations between age_restriction and genre?

• Actors and Directors involved with HBO

• Actors and Directors associated with highest-rated titles

• Distribution of Number of Movies and Shows by Release Year

• Top 10 most popular Movies and Shows

• Correlation between Run-time of Movies and its Popularity?

• Correlation between Genre and Popularity?

• Correlation between the number of movies and shows produced and what country they come from?

Installing packages

# install.packages("rmarkdown")

#load tidyverse to manipulate data
#load ggplot2 for graphing
#load shiny for graphing
#load dplyr to manipulate data
#load knitr for general-purpose literate programming
#load kableExtra to add features to table
#load maps for map graph

library(rmarkdown)  ## used for our output theme 'readthedown'
#render("mydocument.Rmd")
library(tidyverse)
library(ggplot2)
library(shiny)
library(dplyr)
library(countrycode)
library(knitr)
library(kableExtra)
library(maps)

About Our Data

The dataset we have chosen to work with is sourced from Kaggle and is the property of Diego Enrique. Here is the link to access it: https://www.kaggle.com/datasets/dgoenrique/hbo-max-movies-and-tv-shows

Additionally, we would like to provide the link for rmdformats library used for the ‘readthedown’ output theme used in our project, which was created by Julien Barnier. Here’s the link to the theme: https://mran.microsoft.com/snapshot/2019-12-15/web/packages/rmdformats/vignettes/introduction.html

Let us read our datas, shall we?

both_data <- left_join(titles, credits, by = "id")

kable(head(both_data),
      align = "c",
      caption = "<b><center>Sample table of both data",
      format = "html") %>% 
  kable_styling(bootstrap_options = "bordered", full_width = FALSE)

Let’s begin by determining the number or movies and TV shows we are working with

Wow! that’s a lot more movies than shows! But let’s see it visually

What’s the distribution of genres for both Shows and Movies in our dataset?

Now, let us see if there’s a correlation between age_restriction and genres

## Unique age certifications:  PG, G, PG-13, R, TV-G, TV-Y, TV-Y7, TV-PG, NC-17, TV-14, TV-MA, TV-Y7-FV

Well that did not work as expected. Let’s see if a geom_tile graph does the job:

Now lets look at our Actor and Director columns in our credits data.

Here is the distribution of shows and movies available in HBO by release year

Now let’s see what are the top 10 most popular movies and show from imbd and tmdb

Now explore if there’s a relationship between longest movie and its popularity?

Ever wonder if your most favourite genre happens to be the most popular genre on HBO?

Last but not least, let us look at the Number of movies and TV shows by country

Unfortunately, because HBO only got their movies and shows from 99 countries, there are some uncolored countries

Final Summary

Hopefully, after the end of this, we may have created some tools to help you when you can’t figure out what to watch. If you are looking for more variety, you would probably want to stick with movies as you have all types of options to choose from. Same with if you are wanting something newer. Based on our movies and shows by distribution graph, you will definitely have more options from the 1990’s - 2000’s. If you want to stick with a specific genre, our genre and popularity graph will help you with that, for example it shows that the drama genre is the most popular.